Cereal ratings


Suppose you have the following dataset*, which is a list of 80 cereals, containing the following fields:

  • mfr: Manufacturer of cereal

  • A = American Home Food Products

  • G = General Mills

  • K = Kelloggs

  • N = Nabisco

  • P = Post

  • Q = Quaker Oats

  • R = Ralston Purina

  • type:

  • cold

  • hot

  • calories: calories per serving

  • protein: grams of protein per serving

  • fat: grams of fat per serving

  • sodium: milligrams of sodium

  • fiber: grams of dietary fiber

  • carbs: grams of complex carbohydrates

  • sugars: grams of sugars

  • potass: milligrams of potassium

  • vitamins: vitamins and minerals - 0, 25, or 100, indicating the typical percentage of FDA recommended

  • shelf: display shelf (1, 2, or 3, counting from the floor)

  • weight: weight in ounces of one serving

  • cups: number of cups in one serving

  • rating: a rating of the cereals (Possibly from Consumer Reports?)

Given the above, can you build a model using Python to predict cereal rating?

*Dataset source

Click here to view this problem in an interactive Colab (Jupyter) notebook.


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